Characterizing Global Ozonesonde Profile Variability From Surface to the UT/LS With a Clustering Technique and MERRA‐2 Reanalysis
Bibliographic record
Abstract
Abstract Our previous studies employing the self‐organizing map (SOM) clustering technique to ozonesonde data have found significant links among meteorological and chemical regimes, and the shape of the ozone (O3) profile from the troposphere to the lower stratosphere. Those studies, which focused on specific northern hemisphere midlatitude geographical regions, demonstrated the advantages of SOM clustering by quantifying O3 profile variability and the O3/meteorological correspondence. We expand SOM to a global set of ozonesonde profiles spanning 1980 to present from 30 sites to summarize the connections among O3 profiles, meteorology, and chemistry, using the Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA‐2) reanalysis and other ancillary data. Four clusters of O3 mixing ratio profiles from the surface to the upper troposphere/lower stratosphere (UT/LS) are generated for each site, which show dominant profile shapes and typical seasonality (or lack thereof) that generally correspond to latitude (i.e., tropical, subtropical, midlatitude, and polar). Examination of MERRA‐2 output reveals a clear relationship among SOM clusters and covarying meteorological fields (geopotential height, potential vorticity, and tropopause height) for polar and midlatitude sites. However, these relationships break down within ±30° latitude. Carbon monoxide satellite data, along with velocity potential, a proxy for convection, calculated from MERRA‐2 wind fields assist characterization of the tropical and subtropical sites, where biomass burning and convective transport linked to the Madden‐Julian oscillation (MJO) dominate O3 variability. In addition to geophysical characterization of O3 profile variability, these results can be used to evaluate chemical transport model output and satellite measurements of O3 profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".